TY - JOUR A1 - Krämer, Andreas A1 - Durumeric, Aleksander A1 - Charron, Nicholas A1 - Chen, Yaoyi A1 - Clementi, Cecilia A1 - Noé, Frank T1 - Statistically optimal force aggregation for coarse-graining molecular dynamics T2 - The Journal of Physical Chemistry Letters N2 - Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training accurate CG models remains a challenge. A widely used methodology for learning bottom-up CG force fields maps forces from all-atom molecular dynamics to the CG representation and matches them with a CG force field on average. We show that there is flexibility in how to map all-atom forces to the CG representation and that the most commonly used mapping methods are statistically inefficient and potentially even incorrect in the presence of constraints in the all-atom simulation. We define an optimization statement for force mappings and demonstrate that substantially improved CG force fields can be learned from the same simulation data when using optimized force maps. The method is demonstrated on the miniproteins chignolin and tryptophan cage and published as open-source code. Y1 - 2023 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/9346 VL - 14 IS - 17 SP - 3970 EP - 3979 ER -